Seizure detection: evaluation of the Reveal algorithm

Wilson, Scott B.; Scheuer, Mark L.; Emerson, Ronald G.; Gabor, Andrew J.

doi:10.1016/j.clinph.2004.05.018

Cited by 119 publications

(69 citation statements)

References 10 publications

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“…This is a special problem for longer recordings. Until now, these have mostly concentrated on detection of seizures and the spikes and sharp waves which indicate epileptic activity; sensitivities of up to about 79% for seizures and 89% for spikes and sharp waves have been reported for commercial software [42,43].…”

Section: Introductionmentioning

confidence: 99%

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

Lucia

Fritschy

Dayan

et al. 2007

Med Biol Eng Comput

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Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

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Section: Introductionmentioning

confidence: 99%

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

Lucia

Fritschy

Dayan

et al. 2007

Med Biol Eng Comput

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“…Many groups have published methods that automatically detect seizures, but they are predominantly designed for studies in humans [5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy

et al. 2015

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The aim of this prospective blinded study was to evaluate an automated algorithm for spike-andwave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

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“…This Transform is a powerful tool for the analysis of nonstationary signals (Wilson et al, 2004) making it ideal for EEG signal analysis. Its basic principle of operation is extract approximation and detail coefficients of the signal at each decomposition carried out, i.e., get high and low frequencies features of the signal for each level of decomposition.…”

Section: Approaches Based On Wavelet Transformmentioning

confidence: 99%

Automatic Detection of Paroxysms in EEG Signals Using Morphological Descriptors and Artificial Neural Networks

Boos¹,

Scolaro²,

Pereira³

2011

Biomedical Engineering, Trends in Electronics, Communications and Software

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Seizure detection: evaluation of the Reveal algorithm

Cited by 119 publications

References 10 publications

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy

Automatic Detection of Paroxysms in EEG Signals Using Morphological Descriptors and Artificial Neural Networks

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